Instructions to use hf-internal-testing/tiny-random-T5ForConditionalGeneration with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-T5ForConditionalGeneration with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-T5ForConditionalGeneration") model = AutoModelForSeq2SeqLM.from_pretrained("hf-internal-testing/tiny-random-T5ForConditionalGeneration") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- df05a80c1b59e4e5d0d14ab892fa91c9754931ca3bdeaed105de479a9c88261b
- Size of remote file:
- 547 kB
- SHA256:
- 13af40bdc415eca35080f6d8c2f482ecff55bb94eebd056a6f005388c8ba300c
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.